Introduction to Predictive AI Training Course

Introduction

• Defining predictive AI
• Historical background and evolution of predictive analytics
• Core principles of machine learning and data mining

Data Collection and Preprocessing

• Gathering relevant information
• Cleaning and preparing data for analysis
• Understanding different data types and sources

Exploratory Data Analysis (EDA)

• Visualizing data to gain insights
• Descriptive statistics and data summarization techniques
• Identifying patterns and relationships within the data

Statistical Modeling

• Basics of statistical inference
• Regression analysis
• Classification models

Machine Learning Algorithms for Prediction

• Overview of supervised learning algorithms
• Decision trees and random forests
• Neural networks and basics of deep learning

Model Evaluation and Selection

• Understanding model accuracy and performance metrics
• Cross-validation techniques
• Addressing overfitting and model tuning

Practical Applications of Predictive AI

• Case studies across various industries
• Ethical considerations in predictive modeling
• Limitations and challenges of predictive AI

Hands-On Project

• Working with a dataset to build a predictive model
• Applying the model to generate predictions
• Evaluating and interpreting the results

Summary and Next Steps